Non-rigid 3D Model Retrieval Using Set of Local Statistical Features
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Various algorithms for shape-based retrieval of non-rigid 3D models, with invariance to articulation and/or global deformation, have been developed. A majority of these algorithms assumes that 3D models have mathematically well-defined representations, e.g., closed, manifold mesh. These algorithms are thus not applicable to other types of shape models, for example, those defined as polygon soup. This paper proposes a 3D model retrieval algorithm that accepts diverse 3D shape representations and is is able to compare non-rigid 3D models. The algorithm employs a set of hundreds to thousands of 3D, statistical, local features to describe a 3D model. These features are integrated into a feature vector per 3D model by using bag-of-features approach for efficiency in comparing 3D models and for invariance against articulation and global deformation. Experimental evaluation showed that the algorithm performed well for non-rigid 3D model retrieval.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it